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Podracer architectures for scalable Reinforcement Learning


Apr 13, 2021
Matteo Hessel, Manuel Kroiss, Aidan Clark, Iurii Kemaev, John Quan, Thomas Keck, Fabio Viola, Hado van Hasselt


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Muesli: Combining Improvements in Policy Optimization


Apr 13, 2021
Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber, David Silver, Hado van Hasselt


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Discovery of Options via Meta-Learned Subgoals


Feb 12, 2021
Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh


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Discovering Reinforcement Learning Algorithms


Jul 17, 2020
Junhyuk Oh, Matteo Hessel, Wojciech M. Czarnecki, Zhongwen Xu, Hado van Hasselt, Satinder Singh, David Silver


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Meta-Gradient Reinforcement Learning with an Objective Discovered Online


Jul 16, 2020
Zhongwen Xu, Hado van Hasselt, Matteo Hessel, Junhyuk Oh, Satinder Singh, David Silver


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Expected Eligibility Traces


Jul 03, 2020
Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, Diana Borsa


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Self-Tuning Deep Reinforcement Learning


Mar 02, 2020
Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh


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What Can Learned Intrinsic Rewards Capture?


Dec 11, 2019
Zeyu Zheng, Junhyuk Oh, Matteo Hessel, Zhongwen Xu, Manuel Kroiss, Hado van Hasselt, David Silver, Satinder Singh


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Off-Policy Actor-Critic with Shared Experience Replay


Sep 25, 2019
Simon Schmitt, Matteo Hessel, Karen Simonyan


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Discovery of Useful Questions as Auxiliary Tasks


Sep 10, 2019
Vivek Veeriah, Matteo Hessel, Zhongwen Xu, Richard Lewis, Janarthanan Rajendran, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh


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Behaviour Suite for Reinforcement Learning


Aug 13, 2019
Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepezvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado Van Hasselt


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General non-linear Bellman equations


Jul 08, 2019
Hado van Hasselt, John Quan, Matteo Hessel, Zhongwen Xu, Diana Borsa, Andre Barreto


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On Inductive Biases in Deep Reinforcement Learning


Jul 05, 2019
Matteo Hessel, Hado van Hasselt, Joseph Modayil, David Silver


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When to use parametric models in reinforcement learning?


Jun 12, 2019
Hado van Hasselt, Matteo Hessel, John Aslanides


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Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement


Jan 30, 2019
André Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel Mankowitz, Augustin Žídek, Rémi Munos

* Published at ICML 2018 

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Scaling shared model governance via model splitting


Dec 14, 2018
Miljan Martic, Jan Leike, Andrew Trask, Matteo Hessel, Shane Legg, Pushmeet Kohli

* 9 pages 

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Deep Reinforcement Learning and the Deadly Triad


Dec 06, 2018
Hado van Hasselt, Yotam Doron, Florian Strub, Matteo Hessel, Nicolas Sonnerat, Joseph Modayil


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Multi-task Deep Reinforcement Learning with PopArt


Sep 12, 2018
Matteo Hessel, Hubert Soyer, Lasse Espeholt, Wojciech Czarnecki, Simon Schmitt, Hado van Hasselt


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Unicorn: Continual Learning with a Universal, Off-policy Agent


Jul 03, 2018
Daniel J. Mankowitz, Augustin Žídek, André Barreto, Dan Horgan, Matteo Hessel, John Quan, Junhyuk Oh, Hado van Hasselt, David Silver, Tom Schaul


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Observe and Look Further: Achieving Consistent Performance on Atari


May 29, 2018
Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Večerík, Matteo Hessel, Rémi Munos, Olivier Pietquin


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Distributed Prioritized Experience Replay


Mar 02, 2018
Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado van Hasselt, David Silver

* Accepted to International Conference on Learning Representations 2018 

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Rainbow: Combining Improvements in Deep Reinforcement Learning


Oct 06, 2017
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver

* Under review as a conference paper at AAAI 2018 

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The Predictron: End-To-End Learning and Planning


Jul 20, 2017
David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris

* Camera-ready version, ICML 2017, with supplement 

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Learning values across many orders of magnitude


Aug 16, 2016
Hado van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver

* Paper accepted for publication at NIPS 2016. This version includes the appendix 

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Dueling Network Architectures for Deep Reinforcement Learning


Apr 05, 2016
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas

* 15 pages, 5 figures, and 5 tables 

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